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• W2172635911 abstract "Dynamic resource provisioning becomes a practical approach to achieving high thermal and energy efficiency, improving scalability, and optimizing reliability for e-commercial applications running in modern data centers. In this paper, we propose a self-adjusting model called TERN to predict thermal behaviors of hardware resources for client sessions. Our TERN contains two major components: (1) a resource utilization model being responsible for estimating hardware usage based on the number of running client transactions, and (2) a thermal model that discovers correlation between resource utilization and their temperatures. TERN is conducive to predicting thermal trends of diverse workload conditions with a changing transaction mix. We apply the TPC-W benchmark to characterize the resource demands of each type of transactions. Then, we conduct a thermal profiling study of the benchmark with various transaction mixes. TERN judiciously adjusts the models to maintain prediction accuracy for dynamically changing request patterns. Experimental results show that TERN provides a simple yet powerful solution for resource provisioning in thermal-aware data centers where exist rapidly changing workload conditions." @default.
• W2172635911 created "2016-06-24" @default.
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• W2172635911 date "2015-08-01" @default.
• W2172635911 modified "2023-09-26" @default.
• W2172635911 title "TERN: A Self-Adjusting Thermal Model for Dynamic Resource Provisioning in Data Centers" @default.
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• W2172635911 doi "https://doi.org/10.1109/hpcc-css-icess.2015.183" @default.
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